Injection of simulated drugs into a full-body mannequin is frequently performed in simulation-based healthcare training scenarios. Automated drug recognition systems enable objective, real-time sensing and recording of a trainee's performance and, coupled with physiological modeling, can enable the simulator's physical signs to respond automatically and realistically to injected agents.
Current commercial drug recognition strategies based on barcode scanning or RFID tags exhibit some functional limitations. For example, both require an element not present in the actual clinical procedure (such as a barcode or RFID tag attached to the syringe), and both require the labeled or tagged syringe to be brought near a fixed sensor in order for recognition to occur. For example, an RFID antenna built in to the antecubital region of a simulator's arm (front of elbow) will not detect a drug injected into an IV port or manifold located at a distance from the arm, a frequent clinical occurrence. Also, if multiple injections are to be given in rapid sequence, an empty RFID-tagged syringe laid near the arm may interfere with recognition of the next syringe. Additionally, such systems are also not capable of detecting many potential real world errors that may occur. For example, without limitation, such systems are not capable of detecting air embolisms which can result from improper injection technique. Such systems also cannot detect if the total volume of an injected drug has actually entered the body: the failure of the total volume to enter the body may occur, for example, if the drug is injected into a port at a distance from the patient's venous catheter (i.e., via a med line) and the line is not subsequently flushed with saline to ensure that residual drug in the line has been moved into the vein.
As such, there exists a need for improved systems and methods for use in healthcare training scenarios and, more particularly, there exists a need for improved systems and methods for recognizing drug simulants used in healthcare training scenarios.